Semantic Communication Hierarchy for Low-Bandwidth Goal Validation
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Solution Overview
Problem
Distributed machine learning models face challenges in efficiently processing and communicating large activation values, leading to high communication costs and latency, especially in scenarios where edge devices observe partial data and require semantic reasoning without direct access to raw data.
Innovation Solution
A parent device assigns sub-goals to child devices, which process and validate these sub-goals semantically, sending only relevant information or compressed data, using indication bits and confidence levels to reduce communication overhead and improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If distributed machine learning models process and transmit activation values from edge devices, then computing accuracy is improved, but communication costs and latency increase significantly
Solution Approach 1:
The patent extracts only the essential semantic information from the full activation values at edge devices before transmission to the fusion center. This is achieved by having edge devices perform local semantic processing to identify and transmit only the most relevant features and decisions, thereby reducing communication bandwidth requirements while maintaining computing accuracy.
Solution Approach 2:
The patent segments the data transmission process into multiple stages: edge devices first process data locally to extract semantic meaning, then transmit only compressed semantic representations rather than raw activation values. This segmentation of processing and transmission functions reduces the volume of data over the network.
2Loss of information
If edge devices transmit raw sensor data to fusion center, then data completeness is maintained, but communication overhead and processing delay increase
Solution Approach 1:
The patent applies preliminary semantic processing at edge devices before data transmission to the fusion center. Edge devices perform local inference and semantic extraction in advance, transforming raw sensor data into compressed semantic representations. This preliminary action reduces the amount of data that needs to be transmitted and processed later, thereby reducing overall processing delay while maintaining information completeness.
3Loss of energy
If distributed nodes perform local processing to reduce data transmission, then communication costs are reduced, but privacy constraints and data sharing limitations are exacerbated
Solution Approach 1:
The patent enables edge devices to perform self-service by conducting local semantic processing and inference independently. Each edge device processes its own sensor data locally to extract semantic meaning and make decisions, reducing the need to share raw data with the fusion center. This self-service approach maintains privacy while still allowing collaborative decision-making through shared semantic representations.
4Device complexity
If semantic processing is performed at fusion center only, then processing simplicity is maintained, but response time and system efficiency decrease
Solution Approach 1:
The patent introduces dynamic processing where the system automatically determines whether semantic processing should be performed at edge devices or at the fusion center based on the specific task requirements and data characteristics. This dynamic approach allows the system to optimize between processing simplicity and system efficiency by adapting the processing location to current operational needs.
Data Source
AI summary
This disclosure relates to semantic communications. A parent device determines one or more sub-goals based on a goal. The parent device assigns the sub-goals to one or more child devices. Each child device obtains an input, performs semantic extraction on the input to obtain intermediate features, and performs semantic processing on the intermediate features to validate the assigned sub-goal. If the sub-goal is validated, the child device sends a decision flag to its parent device. If the sub-goal cannot be validated, the child device compresses the input by using a neural network, and provide an activation vector output by the neural network to the parent device. The parent device, if receiving a decision flag, may directly validate its own goal based on the decision flag. If receiving an activation vector, the parent device performs semantic extraction and semantic processing on the activation vector to validate its own goal.